Predicting risk of unplanned hospital readmission within 30-days of discharge using machine learning approaches
Unplanned hospital readmissions are a preventable and costly outcome in the health care system. There are limited tools to estimate risk of readmission. The machine learning process offers an opportunity to develop a risk predictor to identify those at high risk of readmission upon discharge. OKAKI has an opportunity to diversify the commercial products it can offer to health care administrators.
View Full Project DescriptionDean Eurich
OKAKI
Life Sciences
Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services
University of Alberta
Accelerate